It will sound like cheating, but it isn't. It's so righteous dude! Multiple imputation (MI) is an effective and responsible way to handle data which is missing at random (MAR). You'll find out what that means too...
Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of MI concepts. (Original Air Date: August, 2014)

Recorded: Fall 2015
Lecturer: Dr. Erin M. Buchanan
This video covers how to check your data for missing data, how much missing data you should consider replacing, what types of data to replace, and how to replace data with the mice package through multiple imputation.
Lecture materials and assignment available at statstools.com.
http://statstools.com/learn/graduate-statistics/
Used in the following courses: Graduate Statistics

Learn how to perform and interpret Little's MCAR test in SPSS. Little's test tests the hypothesis that one's data are missing completely at random, which is an assumption that must be satisfied prior to replacing missing values with various imputation techniques.
Missing value analysis

In this video, we learn how to handle missing values in R: how to find if there are any missing values and remove them. Also, I show how how to work with attributes that can be attached to any R object.
About the series:
Difficulty level: Beginner
This is a brand new tutorial series to learn R Programming Language for Data science / Statistics. I walk you through a structured approach to learn the language so the concepts falls in place perfectly and you gain a clear understanding. This series is filled with end of the lesson exercises and practice exercises to get you hand-on and have fun learning R.
http://rstatistics.net
http://r-statistics.co

Sponsored by the Center for Interdisciplinary Research on AIDS (CIRA) at Yale University's Interdisciplinary Research Methods Core. The presenters are Russell Barbour, Ph.D., CIRA, and Eugenia Buta, Ph.D., CIRA and The Yale Center of Analytical Studies (YCAS).

Get the Full course here:https://www.udemy.com/r-analytics/?couponCode=YOUTUBESPECIAL
Missing data is a common thing in data scientist life. Today we will explore one of the methods for dealing with missing values which will help you for data management and data preparation - Factual Analysis Method.
Use this special coupon to get a YouTube-only discount on the full course:https://www.udemy.com/r-analytics/?couponCode=YOUTUBESPECIAL

Title:Statistical Methods for Bias Adjustment, "Analysis of Missing Data"
Professor Takahiro Hoshino, Department of Economics, Keio University
My focus research topics are statistical causal inference and its applications.
You may not be familiar with the term "causal inference," so let me give you an example.
Let's say we want to find out which is the better way to treat a certain illness: medication or surgery.
As a result of investigation, of the two groups, one medicated and one having had surgery, is it reasonable to conclude that surgery is the better approach to treatment in cases where it offers a far higher survival rate?
If only patients in good overall condition with no complications can undergo surgery, while many patients in poor condition with complications cannot, it may seem that the difference between the survival rates for medication and surgery may be due to the difference in the baseline condition of the patient.
If a patient who has undergone surgery could have also been cured by medication, perhaps medication would be a better approach to treatment than placing a heavy burden on the body with surgery.
【True effects cannot be understood by simple comparison】----------------------------------
The same can also be said of verification of the effects of costly TV advertisements (TV Ads).
In fact, a comparison of two groups, one which has seen a TV Ad for a game application and one which has not,
reveals what first seems to be the opposite effect to the one intended, where the group that has seen the TV Ad used the application for less time and opened the application less times than the group that has not seen the TV Ad.
However, the group that has not seen the TV Ad spends more time using smartphones than watching TV, so actually, the result is natural. Really, the proper evaluation index is "how much application usage time would be decreased if the group that saw the TV Ad had not seen it."
"Usage time had not seen it" is a missing value, known by the term "potential outcome,".
Therefore analysis needs to be performed, factoring in this so-called potential outcome.
Looking at almost all problems in society, true effects cannot be obtained by simple comparison in areas such as evaluation of policies in economics, evaluation of marketing measures and the effects of teaching methods.
My research on related to the development and application of methodology for the performance of correct policy evaluation and statistical causal effect received the Japan Society for the 13th Promotion of Science Prize and Japan Statistical Society Research Achievement Award.
【The analysis of missing data that handles data that cannot be observed】-------------------
Statistical causal inference is one of important fields in missing data analysis that deals with unobservable data that we considered earlier, such as potential "usage time".
Recently, decreasing accuracy of government statistics has become problematic and this has led to calls for development of new indices that combine data from government surveys with big data acquired by companies.
However, because big data is missing data which is biased in that it contains only "in-house purchasing and behavior logs" of a company's own customers, I am working with the Statistics Bureau of the Ministry of Internal Affairs and Communications on the development of new indices that incorporate big data with bias corrected.
No matter how much big data is acquired, because bias that exists in the data may yield incorrect results, The development and application of missing data analysis and statistical data fusion methods are becoming ever-more important in fields such as academic research, government decision-making and corporate marketing practices.
http://www001.upp.so-net.ne.jp/bayesian/Eindex.html

Published on 12/1/2016
Presented on 12/1/2016
Presented by Karen Grace-Martin
You’ve probably heard about many different approaches to dealing with missing data, and you’ve probably gotten different opinions about which one you should use. In this webinar, you’ll get an overview of:
• the three types of missing data, and how they affect the approach to take
• the common approach that is generally worse than any other
• the easy, common, seemingly bad approach that often isn’t so bad, and the situations when it doesn’t work
• the two approaches that give unbiased results, one that is very easy to implement, but only works in limited situations, and one that is harder to implement well, but works with any statistical analysis.

This video provides a general overview of how to utilize AMOS structural equation modeling program to carry out path analysis on a complete dataset (no missing values)
The data for this video can be downloaded from here: https://drive.google.com/open?id=1L-94ToRQqaD1oPaxS0mvSbdZHv-GqXBH
For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below:
Introductory statistics:
https://sites.google.com/view/statisticsfortherealworldagent/home
Multivariate statistics:
https://sites.google.com/view/statistics-for-the-real-world/home

This video demonstrates how to prepare data for use with the Naive Bayes classifier and its cross-validation. It focuses primarily on the selection of suitable variables from a large data set and imputation of missing values. The video also explains the use of Spearman rank correlation for ordinal variables, where the traditional Pearson correlation is not applicable. The lesson is quite informal and avoids more complex statistical concepts.
The data for this lesson can be obtained from the UCI Machine Learning Repository:
* https://archive.ics.uci.edu/ml/datasets/wiki4he
The R source code for this video can be found (some small discrepancies are possible):
* http://visanalytics.org/youtube-rsrc/r-stats/Demo-B3-Imputing-Missing-Values.r
Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.

This is the second video in my series on strategies for dealing with missing data in the context of SEM when using MPLUS. In this video I demonstrate how to invoke Full-information maximum likelihood (FIML) estimation when testing a path analysis model.
A copy of the Word document containing the syntax I review in the video can be downloaded here: https://drive.google.com/open?id=1DZuXKViEfHCjBIQ60l1aGZh3-G1lSuQT
A copy of the original data file (from video 1) can be downloaded here: https://drive.google.com/open?id=1j93yGxqGO8x9DOYt1z7qIADnSot5dX3H
A copy of the .CSV file from the video can be downloaded here: https://drive.google.com/open?id=1vMsGqSqZ0bq7NA9ic6PzaNtOKaDJYasm
IMPORTANT: YOU'LL NEED TO CHANGE THE PATH IN THE DATA: FILE IS LINE IN ORDER TO ENSURE MPLUS WILL READ THE .CSV FILE AM PROVIDING YOU. SEE VIDEO 1 IN THIS SERIES (https://youtube.com/watch?v=tDs8_rcJ5Mk&feature=youtu.be) TO OBTAIN MORE DETAILS ON CREATING .CSV FILES AND READING THEM INTO MPLUS)
The data in this video is based off the raw data that is publicly available from the American National Election Study 2016: http://www.electionstudies.org/studypages/anes_timeseries_2016/anes_timeseries_2016.htm
For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below:
Introductory statistics:
https://sites.google.com/view/statisticsfortherealworldagent/home
Multivariate statistics:
https://sites.google.com/view/statistics-for-the-real-world/home

Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r
Now, we have removed the observation containing a bivariate outlier for age and annual income from the data set. What we did not discuss before is that there are missing inputs (or NA's, which stand for not available) for two variables: employment length and interest rate. In this video we will demonstrate some methods for handling missing data on the employment length variable. You'll practice this newly gained knowledge yourself on the variable interest rate.
First, you want to know how many inputs are missing, as this will affect what you do with them. A simple way of finding out is with the function summary(). If you do this for employment length, you will see that there are 809 NA's.
There are generally three ways to treat missing inputs: delete them, replace them, or keep them. We will illustrate these methods on employment length. When deleting, you can either delete the observations where missing inputs are detected, or delete an entire variable. Typically, you would only want to delete observations if there is just a small number of missing inputs, and would only consider deleting an entire variable when many cases are missing.
Using this construction with which() and is.na(), the rows with missing inputs are deleted in the new data set loan_data_no_NA. To delete the entire variable employment length, you simply set the employment length variable in the loan data equal to NULL. Here, we save the result to a copy of the data set called loan_data_delete_employ. Making a copy of your original data before deleting things can be a good way to avoid losing information, but may be costly if working with very large data sets.
Second, when replacing a variable, common practice is to replace missing values with the median of the values that are actually observed. This is called median imputation.
Last, you can keep the missing values, since in some cases, the fact that a value is missing is important information. Unfortunately, keeping the NAs as such is not always possible, as some methods will automatically delete rows with NAs because they cannot deal with them. So how can we keep NAs? A popular solution is coarse classification.
Using this method, you basically put a continuous variable into so-called bins. Let's start off making a new variable emp_cat, which will be the variable replacing emp_length. The employment length in our data set ranges from 0 to 62 years. We will put employment length into bins of roughly 15 years, with groups 0 to 15, 15 to 30, 30 to 45, 45 plus, and a "missing” group, representing the NAs. Let's see how this changes our data.
Let's look at the plot of this new factor variable. It appears that the bin '0-15' contains a very high proportion of the cases, so it might seem more reasonable to look at bins of different ranges but with similar frequencies, as shown here. You can get these results by trial and error for different bin ranges, or by using quantile functions to know exactly where the breaks should be to get more balanced bins.
Before trying all of this in R yourself, let me finish the video with a couple of remarks. First, all the methods for missing data handling can also be applied to outliers. If you think an outlier is wrong, you can treat it as NA and use any of the methods we have discussed in this chapter.
Second, you may have noticed I only talked about missingness for continuous variables in this chapter. What about factor variables? Here's the basic approach. For categorical variables, deletion works in the exact same way as for continuous variables, deleting either observations or entire variables. When we wish to replace a missing factor variable, this is done by assigning it to the modal class, which is the class with the highest frequency. Keeping NAs for a categorical variable is done by including a missing category.
Now, let's try some of these methods yourself!

Real Life Application
Frequency analysis plays an important role in hydraulic engineering applications such as those concerned with floods, for example in construction of Dams it is necessary to find out the probability of occurring an extreme flood.
Explanation
To understand the concept with clarity it’s important to understand two things:-
1. Probability
2. Return Period
With the basic concept of probability we know that probability of any event is given as Favorable cases by total number of cases.
Return Period also called as Recurrence Interval or Frequency (T) is the time period on an average after which peak flood discharge is likely to be equaled or exceeded.
In order to find the Return Period Plotting Position method is used, in this method the given data is arranged in decreasing order of magnitude and accordingly rank(m) is assigned to each value. The return period for any value is then calculated by following three methods:-
California Formula
Weibull’s Formula
Hazens Formula
Then the probability that a particular value is equal or exceeded is then given by-
Probability=1/T, where T is the Return Period.
We make use of Binomial Event for calculating probability, Binomial even is an event which has only two possible outcomes and is suited for this analysis also as either flood can occur or flood cannot occur. We calculate the probability that a particular event (having probability p) happens exactly r times out of n trials.
The probability of Reliability and Risk is important for us considering the design of hydraulic structures.
Reliability: This is the probability that a particular flood magnitude is never equaled or exceeded in the design life of structure.
Risk: This is the probability that a particular flood magnitude is equaled or exceeded at least once in the design life of structure.
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects.
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Mplus Short Course Topic 11: Regression and Mediation Analysis
Part 9 - Missing Data Analysis
Link to handouts associated with this segment (slides 151-170):
http://www.statmodel.com/download/Aug16_JH_Slides.zip
NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.

On March 1, 2017, Dr. James Peugh from Cincinnati Children’s Hospital Medical Center presented this 90-minute talk at the University of Kentucky on how to handle missing data in Mplus. This was the first presentation in the Spring 2017 Applied Quantitative and Psychometric Series (AQPS). This presentation focused on how to handle missing data for Four SEM-Based Analyses (Categorical CFA with Covariate (MIMIC Model), Moderated Mediation (MacArthur Method), SEM with a Latent Variable Interaction Term, and Multilevel ANCOVA SEM. Visit http://education.uky.edu/edp/apslab/events/#MissingData to download the PowerPoint Handout and Mplus Data Files for this talk.

https://support.sas.com/edu/schedules.html?id=857&ctry=US
Jeff Thompson, a statistical training specialist with SAS Education, provides an overview of the predictive modeling portion of the SAS training course "Applied Analytics Using SAS Enterprise Miner." Thompson also provides a tip on the imputation of missing values.
To learn more about the SAS training course "Applied Analytics Using SAS Enterprise Miner," visit https://support.sas.com/edu/schedules.html?id=857&ctry=US

Presenter: Christopher Fonnesbeck
Description
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to Bayesian methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.
The target audience for the tutorial includes all new Python users, though we recommend that users also attend the NumPy and IPython session in the introductory track.
Tutorial GitHub repo: https://github.com/fonnesbeck/statistical-analysis-python-tutorial
Outline
Introduction to Pandas (45 min)
Importing data
Series and DataFrame objects
Indexing, data selection and subsetting
Hierarchical indexing
Reading and writing files
Date/time types
String conversion
Missing data
Data summarization
Data Wrangling with Pandas (45 min)
Indexing, selection and subsetting
Reshaping DataFrame objects
Pivoting
Alignment
Data aggregation and GroupBy operations
Merging and joining DataFrame objects
Plotting and Visualization (45 min)
Time series plots
Grouped plots
Scatterplots
Histograms
Visualization pro tips
Statistical Data Modeling (45 min)
Fitting data to probability distributions
Linear models
Spline models
Time series analysis
Bayesian models
Required Packages
Python 2.7 or higher (including Python 3)
pandas 0.11.1 or higher, and its dependencies
NumPy 1.6.1 or higher
matplotlib 1.0.0 or higher
pytz
IPython 0.12 or higher
pyzmq
tornado

Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be
Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be
This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.

In this session I show you how you calculate a missing value for an indicator. Sometimes you don't have a number in between a time series. For instance you have a number for 2010 and 2012 but you don't have a number for the year 2011. You do this with interpolation. This session will teach you how to interpolate. You can use the data in a graph, in a policy research note etc. once you have interpolated it.

Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip

0:08 Multiple choice item vs. Likert scale item
1:33 Multiple choice questions with one correct answer
3:27 Multiple choice questions with multiple correct answers
6:03 "Multiple response set" in SPSS
7:52 How to pronounce "Likert"?
This video discusses how to best enter and code multiple choice type data in SPSS as well as how to analyze such data using descriptive stats and multiple response sets.
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#SPSS #Statistics #DataEntry

Importing Data, Checking the Imported Data and Working With Data in R; Dataset: https://goo.gl/tJj5XG
More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT
How to import a datasets into R , How to make sure data was imported correctly into R and How to begin to work with the imported data in R.
▶︎We will learn to use read.table function (which reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file), and some of the arguments such as header argument and sep argument.
▶︎We will learn to use file.choose function to choose a file interactively
▶︎We will discuss how to use Menu options in RStudio to import data into R
▶︎and how to check the imported data to make sure it was imported correctly into R using the dim function to retrieve dimension of an object and let you know the number of rows and columns of the imported data, the head function in R (head() function), which returns the first or last parts of a vector, matrix, table, data frame and will let you see the first several rows of the data, the tail function in R (tail() function) to see the last several rows of the data in R, the double square brackets in R to subset data (brackets lets you select or subset data from a vector, matrix, array, list or data frame) , and the names function in R to get the names of an object in R.
▶︎▶︎ Download the dataset here:
https://statslectures.com/r-stats-datasets
▶︎▶︎Watch More
▶︎Export Data from R (CSV , TXT and other formats): https://bit.ly/2PWS84w
▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg
▶︎Probability Distributions in R: https://bit.ly/2AT3wpI
▶︎Bivariate Analysis in R: https://bit.ly/2SXvcRi
▶︎Linear Regression in R: https://bit.ly/1iytAtm
▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH
◼︎ Topics in the video:
0:00:07 How to read a dataset into R using read.table function and save it as an object
0:00:27 How to access the help menu in R
0:01:02 How to let R know that the first row of our data is headers by using header argument
0:01:14 How to let R know how the observations are separated by using sep argument
0:02:03 How to specify the path to the file using file.choose function
0:03:15 How to use Menu options in R Studio to import data into R
0:05:23 How to prepare the Excel data for importing into R
0:06:15 How to know the dimensions (the number of rows and columns) of the data in R using the dim function
0:06:35 How to see the first several rows of the data using the head command in R
0:06:45 How to see the last several rows of the data in R using the tail function
0:07:18 How to check if the data was read correctly into R using square brackets and subsetting data
0:08:21 How to check the variable names in R using the names function
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
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Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

How much time do you spend preparing data for analysis? For most data analysts, this is a constant chore. See how JMP works to make data preparation easier, faster and more reliable. Learn more about JMP at http://jmp.com/software

Uploading contracts to an online database should not take too long, and with the right solution, there should be a way to quickly drag and drop them into folders. Of course, the contract management team may want to give some thought as to how those folders are categorized. In some industries, it may make sense to classify them by agreement type, whereas in others they may need to be grouped by timeframe or date. It is obviously important to do what makes sense for your company and to ensure everyone understands the classification system that is instituted. With this sort of well-oiled system in place, it is a lot easier to keep a handle on things.

Divide and Conquer.
This is another area that is very industry-dependent, but it is highly unlikely that any company can afford to have an entire contract team devoted to managing one portfolio. More than likely, it is more realistic to divvy up the team and the contracts so that there is a leader for each relevant sphere. The entire team will obviously have to coordinate and communicate, but resources must be allocated in the most efficient manner possible. In turn, this will allow for several individuals to keep an eye on a smaller batch of contracts, thereby facilitating those periodic reviews.
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